论文标题
通过双重注意网络基于双期混合关系市场知识图的库存运动预测
Stock Movement Prediction Based on Bi-typed Hybrid-relational Market Knowledge Graph via Dual Attention Networks
论文作者
论文摘要
股票运输预测（SMP）旨在预测上市公司的股票未来价格趋势，由于金融市场的动荡性，这是一项艰巨的任务。最近的金融研究表明，动量溢出效应在库存波动中起着重要作用。但是，以前的研究通常只学习相关公司之间的简单连接信息，这些公司不可避免地无法对真正的金融市场中上市公司的复杂关系进行建模。为了解决这个问题，我们首先构建了一个更全面的市场知识图（MKG），其中包含双性实体，包括上市公司及其相关高管，以及包括显式关系和隐性关系在内的混合关系。之后，我们提出了DANSMP，这是一个新型的双重注意网络，以根据构成的MKG来学习股票预测的动量溢出信号。我们对九个SOTA基准的构造数据集上的经验实验表明，所提出的DANSMP能够改善用构造的MKG进行库存预测。
Stock Movement Prediction (SMP) aims at predicting listed companies' stock future price trend, which is a challenging task due to the volatile nature of financial markets. Recent financial studies show that the momentum spillover effect plays a significant role in stock fluctuation. However, previous studies typically only learn the simple connection information among related companies, which inevitably fail to model complex relations of listed companies in the real financial market. To address this issue, we first construct a more comprehensive Market Knowledge Graph (MKG) which contains bi-typed entities including listed companies and their associated executives, and hybrid-relations including the explicit relations and implicit relations. Afterward, we propose DanSmp, a novel Dual Attention Networks to learn the momentum spillover signals based upon the constructed MKG for stock prediction. The empirical experiments on our constructed datasets against nine SOTA baselines demonstrate that the proposed DanSmp is capable of improving stock prediction with the constructed MKG.
